This NSF CAREER project aims to enhance electric power grid operators' situational awareness, improve dynamic model quality, and enable online controls to ensure secure power system operation with high penetration of inverter-based resources (IBRs). The project will bring transformative changes to the use of measurements for dynamic state estimation, model deficiency diagnosis and calibration, and measurement configuration, thereby enhancing system reliability and security. This will be achieved through innovative dynamic estimation theories and algorithms that leverage the increasing diversity of sensors and communication infrastructure, as well as advancements in robust estimation, uncertainty quantification, optimization, and data analytics. The intellectual merits of the project include i) a generalized, computationally efficient derivative-free observability theory, with observability indices tailored for dynamic systems with black-box models, ii) integration of Bayesian inference with robust estimation to develop novel nonlinear dynamic estimation methods and iii) a scalable Bayesian framework for dynamic parameter estimation and uncertainty quantification. The broader impacts of the project include developing the next generation of robust dynamic estimation paradigms for IBR-dominated power systems, and industry-academia collaborative initiatives to promote industry-driven research, course renovation, and training to equip students (including K-12 students, and those